EMCNet : Graph-Nets for Electron Micrographs Classification
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910597225906176 |
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| author | Srinivas, Sakhinana Sagar Sarkar, Rajat Kumar Runkana, Venkataramana |
| author_facet | Srinivas, Sakhinana Sagar Sarkar, Rajat Kumar Runkana, Venkataramana |
| contents | Characterization of materials via electron micrographs is an important and challenging task in several materials processing industries. Classification of electron micrographs is complex due to the high intra-class dissimilarity, high inter-class similarity, and multi-spatial scales of patterns. However, existing methods are ineffective in learning complex image patterns. We propose an effective end-to-end electron micrograph representation learning-based framework for nanomaterial identification to overcome the challenges. We demonstrate that our framework outperforms the popular baselines on the open-source datasets in nanomaterials-based identification tasks. The ablation studies are reported in great detail to support the efficacy of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03767 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EMCNet : Graph-Nets for Electron Micrographs Classification Srinivas, Sakhinana Sagar Sarkar, Rajat Kumar Runkana, Venkataramana Computer Vision and Pattern Recognition Machine Learning Characterization of materials via electron micrographs is an important and challenging task in several materials processing industries. Classification of electron micrographs is complex due to the high intra-class dissimilarity, high inter-class similarity, and multi-spatial scales of patterns. However, existing methods are ineffective in learning complex image patterns. We propose an effective end-to-end electron micrograph representation learning-based framework for nanomaterial identification to overcome the challenges. We demonstrate that our framework outperforms the popular baselines on the open-source datasets in nanomaterials-based identification tasks. The ablation studies are reported in great detail to support the efficacy of our approach. |
| title | EMCNet : Graph-Nets for Electron Micrographs Classification |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.03767 |